Simple, Inexpensive Method for Automating Tissue Microarray Production Provides Enhanced Microarray Reproducibility
Abstract
Tissue microarrays are a novel technology with the potential to impact cancer research by reducing the time, materials, and costs related to specimen-based marker validation. The process uses small cores of specimen tissue for molecular studies, maximizing the quantity of specimens that can be analyzed on a single slide and the results that can be obtained from a single antibody study. However, this process can be tedious and requires a significant time commitment for array production, particularly for the hand-produced tissue array blocks. In addition, this process has significant repetitive motions, risking repetitive stress injury for technical personnel. For these reasons, we have sought a simple, inexpensive system for automation of the existing microarray technologies. Using this system, slides containing as many as 400 specimens can be constructed in a simple and reproducible manner. Automation of the tissue microarray apparatus is accomplished by attaching two stepper motors to the micrometers of the apparatus that control array movement, and it has the advantages of standardizing the spacing between each specimen and eliminating repetitive motions by the user. A computer program is used to run the motors, allowing the user to input commands based on the desired moving distance. After assimilation of the motors, motor control boards, and corresponding program, the final product was tested and demonstrated to provide consistent, reproducible operation. Tissue microarrays were generated with specimen tissue diameters of 1.5 mm, 1.0 mm, and 0.6 mm with core densities upwards of 300 samples per slide.
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Routine availability of digitized pathology images, coupled with well-known issues associated with inter-observer variability in how pathologists interpret studies, has led to increased interest in computer-assisted decision support systems.
“Routine availability of digitized pathology images, coupled with well-known issues associated with inter-observer variability in how pathologists interpret studies [159], has led to increased interest in computer-assisted decision support systems.”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [159]
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Foran, D. J., Yang, L., Chen, W., Hu, J., Goodell, L. A., Reiss, M., Wang, F., Kurc, T., Pan, T., Sharma, A., et al. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000170 · Cited by 65 · see citing works
- Authors
- Matysiak, B. E., Brodzeller, T., Buck, S., French, A., Counts, C., Boorsma, B., Datta, M. W., & Kajdacsy-Balla, A. A.
- Affiliations
- Marquette University (US)
- Corresponding author
- Matysiak, Brian E.
- Brodzeller, Tracy
- Datta, Milton W.
- Venue
- Applied Immunohistochemistry & Molecular Morphology
- Published
- 2003-09
- Volume
- 11
- Issue
- 3
- Pages
- 269-273
- ISSN
- 1533-4058 (ISSN-L)
- 1541-2016
- OpenAlex
- W2084298700
- Cited by
- 23
- References
- 0
- Citations by year
- 2025: 1 · 2024: 1 · 2020: 2 · 2018: 1 · 2017: 1 · 2014: 1 · 2013: 1 · 2012: 1
- FWCI
- 2.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied immunohistochemistry & molecular morphology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- tissue microarray, stepper motor, low-cost laboratory equipment, core density
- MeSH terms
- Automation, Pathology, Reproducibility of Results
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